Geospatial Demand Forecasting for Optimized Resource Allocation

We developed a demand forecasting model for transportation services that leverages historical data to optimize vehicle placement across city zones. The model incorporates several critical factors:

Seasonality: Captures variations in demand due to time of year, day of week, and time of day.

Event Impacts: Accounts for demand fluctuations caused by events such as concerts, sporting matches, or festivals.

Weather Conditions: Integrates the effect of weather patterns on transportation demand.

Implementing this solution has directly enhanced the profitability of transportation companies by:

Increasing Order Volume: More accurate demand predictions facilitate better resource allocation, leading to a higher number of completed orders.

Reducing Vehicle Downtime: Optimized vehicle placement minimizes idle time, improving fleet efficiency.

Methodology: Demand pattern recognition within historical data using a sliding window approach frames the problem as multivariate time series forecasting, with careful consideration of both daily and weekly seasonality.

Data: The model utilizes historical demand data, quantitative weather data (rainfall, temperature, wind speed, etc.), and event indicators (weekday/holiday). Weather data is obtained from reliable third-party APIs.

Implementation: A neural network architecture was trained on the combined dataset to deliver accurate demand predictions.